The latest improvements in optical communications include always digital signal processing assistance. Machine learning and neural networks were also incorporated, to enforce problem-solving in tasks that are demanding in terms of complexity and computational speed. These algorithms aim at expanding the reach of fiber transmission channels, improving data recovery and optical routing. In this context, reservoir computing (RC) inspired hardware photonic implementations that can offer revolutionary solutions. The latter were initially tested in wireless channel equalization. Lately, they were endorsed for modulation format identification, optical header recognition, and data recovery. Here, I discuss the requirements that analog, photonic RC implementations need to fulfil for real-time signal processing in optical communications. While offline processing was successfully demonstrated, the challenges to address the data rates of the latest communication protocols are still significant.
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